What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Mathematical analysis
Tony Lindeberg spends much of his time researching Artificial intelligence, Computer vision, Scale space, Image processing and Smoothing.
His work on Blob detection as part of general Artificial intelligence research is frequently linked to System testing, thereby connecting diverse disciplines of science.
His studies examine the connections between Computer vision and genetics, as well as such issues in Invariant, with regards to Scaling, Local binary patterns, GLOH, Principal curvature-based region detector and Scale-invariant feature transform.
His studies deal with areas such as Representation, Edge detection and Pattern recognition as well as Scale space.
His Image processing research is multidisciplinary, incorporating perspectives in Motion estimation and Histogram.
His study in Smoothing is interdisciplinary in nature, drawing from both Algorithm, Discrete measure, Discrete modelling and Mathematical analysis.
His most cited work include:
- Feature Detection with Automatic Scale Selection (2305 citations)
- Scale-space theory in computer vision (1839 citations)
- Scale-Space Theory : A Basic Tool for Analysing Structures at Different Scales (989 citations)
What are the main themes of his work throughout his whole career to date?
Tony Lindeberg mainly investigates Artificial intelligence, Scale space, Computer vision, Algorithm and Pattern recognition.
His study involves Image, Receptive field, Image processing, Histogram and Feature, a branch of Artificial intelligence.
His Scale space study combines topics in areas such as Smoothing, Maxima and minima, Sketch, Representation and Edge detection.
His work investigates the relationship between Smoothing and topics such as Theoretical computer science that intersect with problems in Gaussian function.
His work in Computer vision addresses issues such as Point, which are connected to fields such as Line.
As part of the same scientific family, Tony Lindeberg usually focuses on Algorithm, concentrating on Scale invariance and intersecting with Scaling, Discrete mathematics and Transformation.
He most often published in these fields:
- Artificial intelligence (63.10%)
- Scale space (43.45%)
- Computer vision (42.26%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (63.10%)
- Scale invariance (12.50%)
- Algorithm (23.81%)
In recent papers he was focusing on the following fields of study:
Tony Lindeberg focuses on Artificial intelligence, Scale invariance, Algorithm, Receptive field and Pattern recognition.
His study connects Cognitive science and Artificial intelligence.
His Scale invariance study integrates concerns from other disciplines, such as Maxima and minima, Scale space, Transformation, Kernel and Scaling.
His Scale space study combines topics from a wide range of disciplines, such as Differential invariant, Norm and Second moment of area.
His Receptive field research is multidisciplinary, incorporating elements of Theory of computation, Domain, Computer vision and Affine transformation.
His Pattern recognition research includes themes of Histogram, Texture and Transformer.
Between 2013 and 2021, his most popular works were:
- Image Matching Using Generalized Scale-Space Interest Points (95 citations)
- Time-Causal and Time-Recursive Spatio-Temporal Receptive Fields (15 citations)
- Idealized computational models for auditory receptive fields (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Mathematical analysis
Tony Lindeberg mainly focuses on Scale invariance, Scale space, Algorithm, Covariance and Maxima and minima.
Tony Lindeberg merges Scale space with Temporal scales in his research.
Tony Lindeberg has included themes like Uniform distribution and Kernel in his Algorithm study.
His Covariance research is multidisciplinary, relying on both Range, Differential, Image and Representation.
His Feature study is concerned with the field of Artificial intelligence as a whole.
Many of his studies involve connections with topics such as Auditory perception and Artificial intelligence.
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